Abstract: Background: Chronic obstructive pulmonary disease (COPD) and asthma have a high prevalence and disease burden. Blended self-management interventions, which combine eHealth with face-to-face interventions, can help reduce the disease burden. Objective: This systematic review and meta-analysis aims to examine the effectiveness of blended self-management interventions on health-related effectiveness and process outcomes for people with COPD or asthma. Methods: PubMed, Web of Science, COCHRANE Library, Emcare, and Embase were searched in December 2018 and updated in November 2020. Study quality was assessed using the Cochrane risk of bias (ROB) 2 tool and the Grading of Recommendations, Assessment, Development, and Evaluation. Results: A total of 15 COPD and 7 asthma randomized controlled trials were included in this study. The meta-analysis of COPD studies found that the blended intervention showed a small improvement in exercise capacity (standardized mean difference [SMD] 0.48; 95% CI 0.10-0.85) and a significant improvement in the quality of life (QoL; SMD 0.81; 95% CI 0.11-1.51). Blended intervention also reduced the admission rate (relative ratio [RR] 0.61; 95% CI 0.38-0.97). In the COPD systematic review, regarding the exacerbation frequency, both studies found that the intervention reduced exacerbation frequency (RR 0.38; 95% CI 0.26-0.56). A large effect was found on BMI (d=0.81; 95% CI 0.25-1.34); however, the effect was inconclusive because only 1 study was included. Regarding medication adherence, 2 of 3 studies found a moderate effect (d=0.73; 95% CI 0.50-0.96), and 1 study reported a mixed effect. Regarding self-management ability, 1 study reported a large effect (d=1.15; 95% CI 0.66-1.62), and no effect was reported in that study. No effect was found on other process outcomes. The meta-analysis of asthma studies found that blended intervention had a small improvement in lung function (SMD 0.40; 95% CI 0.18-0.62) and QoL (SMD 0.36; 95% CI 0.21-0.50) and a moderate improvement in asthma control (SMD 0.67; 95% CI 0.40-0.93). A large effect was found on BMI (d=1.42; 95% CI 0.28-2.42) and exercise capacity (d=1.50; 95% CI 0.35-2.50); however, 1 study was included per outcome. There was no effect on other outcomes. Furthermore, the majority of the 22 studies showed some concerns about the ROB, and the quality of evidence varied. Conclusions: In patients with COPD, the blended self-management interventions had mixed effects on health-related outcomes, with the strongest evidence found for exercise capacity, QoL, and admission rate. Furthermore, the review suggested that the interventions resulted in small effects on lung function and QoL and a moderate effect on asthma control in patients with asthma. There is some evidence for the effectiveness of blended self-management interventions for patients with COPD and asthma; however, more research is needed. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42019119894; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=119894
Rationale: The dietary protein counselling of the VITAMIN trial showed to be effective in increasing the protein intake in community-dwelling older adults up to 1.41 g/kg/day after 6-months intervention and sustaining this intake up to 1.24 g/kg/day at 12-months. In this sub-analysis we determine how the increased protein intake was established. Methods: The VITAMIN (VITal AMsterdam older adults IN the city) RCT randomised older adults into: control, exercise, or exercise plus dietary counselling (protein) group. The dietary counselling intervention was blended, by use of face-to-face contacts and videoconferencing during a 6-month intervention, followed by a 6-month follow-up. Dietary intake was measured by a 3d dietary record at 0, 6 and 12 months (m). Sub-group analysis included characterisation of protein sources, product groups, resulting amino acid intake, and intake per meal moment. Linear Mixed Models were performed with SPSSv25; whereas time and time*group interaction were defined as fixed factors, and the protein group as reference.Results: In total 212 subjects were eligible for analysis (72.2 ± 6.3y), with an average protein intake at baseline of 77.8 (20) g/day and 1.08 (0.3) g/kg/day. Animal protein (g) accounted as major source (6m +25.6 (2.7) p<0.001 | 12m +15.6 (2.8) p<0.001), with the main increase in dairy products (g) (6m +14.2 (1.5) p<0.001 | 12m +10.0 (1.5) p<0.001), followed by fish and meat. This resulted in significant changes in amino acid intake: e.g. leucine (g) 6m +2.3 (0.3) p<0.001 | 12m +1.1 (0.3) p<0.001. Significant increased intake for the protein group was seen at all 6 meal moments, and particularly at breakfast (g) 6m +6.2 (1.0) p<0.001 | 12m +6.5 (1.1) p<0.001) and lunch (g) 6m +7.2 (1.3) p<0.001 | 12m +4.0 (1.3) p=0.003.Conclusions: Blended dietary counselling was effective in increasing protein intake in a lifestyle intervention in community-dwelling older adults. This was predominantly achieved by consuming more animal protein sources, particularly dairy products, and especially during breakfast and lunch. Keywords: Ageing, Behaviour change, E-health, Nutrition, Protein
Background/purpose: For prevention of sarcopenia and functionaldecline in community-dwelling older adults, a higher daily proteinintake is needed. A new e-health strategy for dietary counselling wasused with the aim to increase total daily protein intake to optimallevels (minimal 1.2 g/kg/day, optimal 1.5 g/kg/day) through use ofregular food products.Methods: The VITAMIN (VITal Amsterdam older adults IN the city)RCT included 245 community-dwelling older adults (age ≥ 55y):control, exercise, and exercise plus dietary counselling (protein)group. The dietary counselling intervention was based on behaviourchange and personalization. Dietary intake was measured by a 3ddietary record at baseline, after 6-month intervention and 12-monthfollow-up. The primary outcome was average daily protein intake(g/kg/day). Sub-group analysis and secondary outcomes includeddaily protein distribution, sources, product groups. A Linear MixedModels (LMM) of repeated measures was performed with STATAv13.Results: Mean age of the 224 subjects was 72.0(6.5) years, a BMI of26.0(4.2). The LMM showed a significant effect of time and time*group(p<0.001). The dietary counselling group showed higher protein intakethan either control (1.41 vs 1.13 g/kg/day; β +0.32; p<0.001) or exercisegroup (1.41 vs 1.11 g/kg/day; β +0.33; p<0.001) after 6-month interventionand 12-month follow-up.Conclusions and implications: This study shows digitally supporteddietary counselling improves protein intake sufficiently in communitydwellingolder adults with use of regular food products. Protein intakeincrease by personalised counselling with e-health is a promising strategyfor dieticians.
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
